---
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: sentiment_deberta
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# sentiment_deberta

This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7123
- Accuracy: 0.6938
- F1: 0.6401
- Precision: 0.6262
- Recall: 0.6854

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.087         | 1.0   | 47   | 1.1008          | 0.2551   | 0.3042 | 0.4734    | 0.4956 |
| 0.9933        | 2.0   | 94   | 0.9692          | 0.5545   | 0.5098 | 0.5126    | 0.5496 |
| 0.8709        | 3.0   | 141  | 0.9352          | 0.5003   | 0.5003 | 0.5301    | 0.5804 |
| 0.8444        | 4.0   | 188  | 0.8729          | 0.5874   | 0.5602 | 0.5671    | 0.6204 |
| 0.7833        | 5.0   | 235  | 0.9394          | 0.4778   | 0.4980 | 0.5643    | 0.6353 |
| 0.7003        | 6.0   | 282  | 0.7279          | 0.6834   | 0.6306 | 0.6150    | 0.6828 |
| 0.6383        | 7.0   | 329  | 0.7808          | 0.6390   | 0.6123 | 0.6073    | 0.7007 |
| 0.5996        | 8.0   | 376  | 0.7379          | 0.6802   | 0.6367 | 0.6231    | 0.6993 |
| 0.5514        | 9.0   | 423  | 0.7846          | 0.6745   | 0.6204 | 0.6015    | 0.6901 |
| 0.4837        | 10.0  | 470  | 0.7123          | 0.6938   | 0.6401 | 0.6262    | 0.6854 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1